INDI: a computational framework for inferring drug interactions and their associated recommendations.

Abstract

Inferring drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Most computational inference methods focus on modeling drug pharmacokinetics, aiming at interactions that result from a common metabolizing enzyme (CYP). Here, we introduce a novel prediction method, INDI (INferring Drug Interactions), allowing the inference of both pharmacokinetic, CYP-related DDIs (along with their associated CYPs) and pharmacodynamic, non-CYP associated ones. On cross validation, it obtains high specificity and sensitivity levels (AUC (area under the receiver-operating characteristic curve) ≥0.93). In application to the FDA adverse event reporting system, 53% of the drug events could potentially be connected to known (41%) or predicted (12%) DDIs. Additionally, INDI predicts the severity level of each DDI upon co-administration of the involved drugs, suggesting that severe interactions are abundant in the clinical practice. Examining regularly taken medications by hospitalized patients, 18% of the patients receive known or predicted severely interacting drugs and are hospitalized more frequently. Access to INDI and its predictions is provided via a web tool at http://www.cs.tau.ac.il/~bnet/software/INDI, facilitating the inference and exploration of drug interactions and providing important leads for physicians and pharmaceutical companies alike.

Illustration of INDI algorithm: computation of drug–drug similarity measures (e.g., (A); scoring query drug pairs according to their similarity to known DDIs (B); and integration of the similarities to classification features and subsequent interaction prediction (C).

Networks of CRDs (A) and NCRDs (B) with severe recommendations between third level ATC classes. Black solid lines denote interactions supported by both known and predicted interactions, while red dashed lines denote interactions supported by predicted interactions only. Edge width corresponds to the average percentage of interacting drugs from each class and node size corresponds to the number of interactors (degree) of the class. The first letter of each ATC category denotes the top level, anatomical, class, including Alimentary tract and metabolism (A), Blood and blood forming organs (B), Cardiovascular system (C), Dermatologicals (D), Genito-urinary system and sex hormones (G), Systemic hormonal preparations, excluding sex hormones and insulins (H), Antiinfectives for systemic use (J), Antineoplastic and immunomodulating agents (L), Musculo-skeletal system (M), Nervous system (N), Antiparasitic products, insecticides and repellents (P), Respiratory system (R) and Sensory organs (S).